An initial prediction and fine-tuning model based on improving GCN for 3D human motion prediction

Area of Science:

• Computer Vision
• Machine Learning
• Human Motion Analysis

Background:

• Deep learning has advanced human motion prediction, but long-term forecasting and skeletal naturalness remain challenging.
• Existing methods struggle with predicting accurate future poses and avoiding unnatural skeletal deformations.

Purpose of the Study:

• To propose a novel GCN-based two-stage method for accurate and natural long-term human motion prediction.
• To address limitations in predicting skeletal deformation and ensure realistic human movement sequences.

Main Methods:

• A two-stage approach using Graph Convolutional Networks (GCNs).
• Stage 1: A prediction model with cascaded spatial attention graph convolution layers (SAGCL) for initial motion sequence generation.
• Stage 2: A fine-tuning model with causally temporal-graph convolution layers (CT-GCL) using spatial coordinate and bone length error loss functions.

Main Results:

• The proposed two-stage method significantly outperforms state-of-the-art methods on Human3.6m and CMU-MoCap datasets.
• The fine-tuning stage effectively corrects unnatural skeletal deformations, producing more realistic motion sequences.
• Demonstrated superior performance in long-term human motion prediction tasks.

Conclusions:

• The GCN-based two-stage prediction method offers a robust solution for accurate and natural long-term human motion prediction.
• The combination of initial prediction and fine-tuning effectively tackles challenges in skeletal deformation.
• Further research is needed to explore limitations and achieve future breakthroughs in the field.